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The co-occurrence of uranyl and arsenate in contaminated water caused by natural processes and mining is a concern for impacted communities, including in Native American lands in the U.S. Southwest. We investigated the simultaneous removal of aqueous uranyl and arsenate after the reaction with limestone and precipitated hydroxyapatite (HAp, Ca10(PO4)6(OH)2). In benchtop experiments with an initial pH of 3.0 and initial concentrations of 1 mM U and As, uranyl and arsenate coprecipitated in the presence of 1 g L−1 limestone. However, related experiments initiated under circumneutral pH conditions showed that uranyl and arsenate remained soluble. Upon addition of 1 mM PO43− and 3 mM Ca2+ in solution (initial concentration of 0.05 mM U and As) resulted in the rapid removal of over 97% of U via Ca−U−P precipitation. In experiments with 2 mM PO4 3− and 10 mM Ca2+ at pH rising from 7.0 to 11.0, aqueous concentrations of As decreased (between 30 and 98%) circa pH 9. HAp precipitation in solids was confirmed by powder X-ray diffraction and scanning electron microscopy/energy dispersive X-ray. Electron microprobe analysis indicated U was coprecipitated with Ca and P, while As was mainly immobilized through HAp adsorption. The results indicate that natural materials, such as HAp and limestone, can effectively remove uranyl and arsenate mixtures. 

Abstract The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent “messiness” of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation–hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time. 

A simple and efficient target preparation method is developed combining spin coating and solution combustion synthesis. Multiple smooth and uniform UO₂targets have been prepared using this method on a variety of backings (aluminium, carbon, silicon) used in nuclear physics experiments. The thicknesses of the targets can be precisely tuned by changing the number of coatings within the range of ~50-1000 µm/cm². These targets are highly uniform (<5% deviation), robust, and remain strongly adherent to their backings even after being irradiated by high doses (10¹⁷ions/ cm²) of 1.7 MeV Ar²⁺ions.